TY - JOUR
T1 - Morphological classification of brains via high-dimensional shape transformations and machine learning methods
AU - Lao, Zhiqiang
AU - Shen, Dinggang
AU - Xue, Zhong
AU - Karacali, Bilge
AU - Resnick, Susan M.
AU - Davatzikos, Christos
N1 - Funding Information:
The datasets used in the experiments were obtained under the Baltimore Longitudinal Study of Aging (BLSA). This work was supported in part by NIH grant R01 AG14971 and by NIH contract AG-93-07.
PY - 2004/1
Y1 - 2004/1
N2 - A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.
AB - A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.
KW - High-dimensional shape transformations
KW - Machine learning methods
KW - Morphological classification
UR - http://www.scopus.com/inward/record.url?scp=1642574430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=1642574430&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2003.09.027
DO - 10.1016/j.neuroimage.2003.09.027
M3 - Article
C2 - 14741641
AN - SCOPUS:1642574430
SN - 1053-8119
VL - 21
SP - 46
EP - 57
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -